Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations134323
Missing cells384881
Missing cells (%)13.6%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory21.5 MiB
Average record size in memory168.0 B

Variable types

Numeric8
Categorical8
Text5

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
batsman_runs is highly overall correlated with total_runsHigh correlation
bye_runs is highly overall correlated with dismissal_kindHigh correlation
dismissal_kind is highly overall correlated with bye_runs and 1 other fieldsHigh correlation
extra_runs is highly overall correlated with legbye_runs and 2 other fieldsHigh correlation
inning is highly overall correlated with is_super_overHigh correlation
is_super_over is highly overall correlated with inningHigh correlation
legbye_runs is highly overall correlated with extra_runsHigh correlation
penalty_runs is highly overall correlated with dismissal_kind and 1 other fieldsHigh correlation
total_runs is highly overall correlated with batsman_runsHigh correlation
wide_runs is highly overall correlated with extra_runsHigh correlation
is_super_over is highly imbalanced (99.3%) Imbalance
bye_runs is highly imbalanced (98.7%) Imbalance
noball_runs is highly imbalanced (98.3%) Imbalance
penalty_runs is highly imbalanced (> 99.9%) Imbalance
player_dismissed has 127664 (95.0%) missing values Missing
dismissal_kind has 127664 (95.0%) missing values Missing
fielder has 129543 (96.4%) missing values Missing
wide_runs has 130254 (97.0%) zeros Zeros
legbye_runs has 131967 (98.2%) zeros Zeros
batsman_runs has 55004 (40.9%) zeros Zeros
extra_runs has 126959 (94.5%) zeros Zeros
total_runs has 47961 (35.7%) zeros Zeros

Reproduction

Analysis started2025-04-18 20:20:35.266578
Analysis finished2025-04-18 20:20:52.582800
Duration17.32 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

match_id
Real number (ℝ)

Distinct567
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.18246
Minimum1
Maximum567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-04-18T20:20:52.686834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1144
median284
Q3425
95-th percentile538
Maximum567
Range566
Interquartile range (IQR)281

Descriptive statistics

Standard deviation163.11233
Coefficient of variation (CV)0.57397043
Kurtosis-1.1960422
Mean284.18246
Median Absolute Deviation (MAD)140
Skewness-0.0063084121
Sum38172241
Variance26605.633
MonotonicityIncreasing
2025-04-18T20:20:52.823997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 267
 
0.2%
34 263
 
0.2%
534 262
 
0.2%
476 262
 
0.2%
388 261
 
0.2%
190 259
 
0.2%
401 258
 
0.2%
536 258
 
0.2%
257 257
 
0.2%
211 257
 
0.2%
Other values (557) 131719
98.1%
ValueCountFrequency (%)
1 248
0.2%
2 247
0.2%
3 218
0.2%
4 247
0.2%
5 248
0.2%
6 216
0.2%
7 254
0.2%
8 212
0.2%
9 226
0.2%
10 239
0.2%
ValueCountFrequency (%)
567 134
0.1%
566 126
0.1%
565 220
0.2%
564 248
0.2%
563 247
0.2%
562 248
0.2%
561 244
0.2%
560 246
0.2%
559 239
0.2%
558 247
0.2%

inning
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
69592 
2
64650 
3
 
43
4
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134323
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 69592
51.8%
2 64650
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

Length

2025-04-18T20:20:52.943229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T20:20:53.030366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 69592
51.8%
2 64650
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 69592
51.8%
2 64650
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134323
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 69592
51.8%
2 64650
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 134323
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 69592
51.8%
2 64650
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 69592
51.8%
2 64650
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

batting_team
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Mumbai Indians
16931 
Kings XI Punjab
15810 
Royal Challengers Bangalore
15470 
Delhi Daredevils
15417 
Chennai Super Kings
15282 
Other values (8)
55413 

Length

Max length27
Median length21
Mean length17.865347
Min length13

Characters and Unicode

Total characters2399727
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunrisers Hyderabad
2nd rowSunrisers Hyderabad
3rd rowSunrisers Hyderabad
4th rowSunrisers Hyderabad
5th rowSunrisers Hyderabad

Common Values

ValueCountFrequency (%)
Mumbai Indians 16931
12.6%
Kings XI Punjab 15810
11.8%
Royal Challengers Bangalore 15470
11.5%
Delhi Daredevils 15417
11.5%
Chennai Super Kings 15282
11.4%
Kolkata Knight Riders 15277
11.4%
Rajasthan Royals 13672
10.2%
Deccan Chargers 9034
6.7%
Sunrisers Hyderabad 6830
5.1%
Pune Warriors 5443
 
4.1%
Other values (3) 5157
 
3.8%

Length

2025-04-18T20:20:53.135909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 31092
 
9.3%
mumbai 16931
 
5.1%
indians 16931
 
5.1%
xi 15810
 
4.7%
punjab 15810
 
4.7%
royal 15470
 
4.6%
challengers 15470
 
4.6%
bangalore 15470
 
4.6%
delhi 15417
 
4.6%
daredevils 15417
 
4.6%
Other values (20) 160149
48.0%

Most occurring characters

ValueCountFrequency (%)
a 273078
 
11.4%
n 199899
 
8.3%
199644
 
8.3%
e 182637
 
7.6%
i 162854
 
6.8%
s 156407
 
6.5%
r 138542
 
5.8%
l 123245
 
5.1%
g 90143
 
3.8%
h 85734
 
3.6%
Other values (27) 787544
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1850306
77.1%
Uppercase Letter 349777
 
14.6%
Space Separator 199644
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 273078
14.8%
n 199899
10.8%
e 182637
9.9%
i 162854
8.8%
s 156407
8.5%
r 138542
 
7.5%
l 123245
 
6.7%
g 90143
 
4.9%
h 85734
 
4.6%
o 68589
 
3.7%
Other values (11) 369178
20.0%
Uppercase Letter
ValueCountFrequency (%)
K 64810
18.5%
R 59991
17.2%
D 39868
11.4%
C 39786
11.4%
I 32741
9.4%
S 24012
 
6.9%
P 23153
 
6.6%
M 16931
 
4.8%
X 15810
 
4.5%
B 15470
 
4.4%
Other values (5) 17205
 
4.9%
Space Separator
ValueCountFrequency (%)
199644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2200083
91.7%
Common 199644
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 273078
 
12.4%
n 199899
 
9.1%
e 182637
 
8.3%
i 162854
 
7.4%
s 156407
 
7.1%
r 138542
 
6.3%
l 123245
 
5.6%
g 90143
 
4.1%
h 85734
 
3.9%
o 68589
 
3.1%
Other values (26) 718955
32.7%
Common
ValueCountFrequency (%)
199644
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2399727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 273078
 
11.4%
n 199899
 
8.3%
199644
 
8.3%
e 182637
 
7.6%
i 162854
 
6.8%
s 156407
 
6.5%
r 138542
 
5.8%
l 123245
 
5.1%
g 90143
 
3.8%
h 85734
 
3.6%
Other values (27) 787544
32.8%

bowling_team
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Mumbai Indians
16735 
Kings XI Punjab
15667 
Royal Challengers Bangalore
15552 
Kolkata Knight Riders
15535 
Delhi Daredevils
15482 
Other values (8)
55352 

Length

Max length27
Median length21
Mean length17.881807
Min length13

Characters and Unicode

Total characters2401938
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowRoyal Challengers Bangalore
3rd rowRoyal Challengers Bangalore
4th rowRoyal Challengers Bangalore
5th rowRoyal Challengers Bangalore

Common Values

ValueCountFrequency (%)
Mumbai Indians 16735
12.5%
Kings XI Punjab 15667
11.7%
Royal Challengers Bangalore 15552
11.6%
Kolkata Knight Riders 15535
11.6%
Delhi Daredevils 15482
11.5%
Chennai Super Kings 15064
11.2%
Rajasthan Royals 13852
10.3%
Deccan Chargers 9039
6.7%
Sunrisers Hyderabad 6758
5.0%
Pune Warriors 5457
 
4.1%
Other values (3) 5182
 
3.9%

Length

2025-04-18T20:20:53.248322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 30731
 
9.2%
mumbai 16735
 
5.0%
indians 16735
 
5.0%
xi 15667
 
4.7%
punjab 15667
 
4.7%
royal 15552
 
4.7%
challengers 15552
 
4.7%
bangalore 15552
 
4.7%
kolkata 15535
 
4.7%
knight 15535
 
4.7%
Other values (20) 160745
48.1%

Most occurring characters

ValueCountFrequency (%)
a 273856
 
11.4%
199683
 
8.3%
n 199165
 
8.3%
e 182900
 
7.6%
i 162552
 
6.8%
s 156547
 
6.5%
r 138704
 
5.8%
l 124173
 
5.2%
g 90265
 
3.8%
h 86138
 
3.6%
Other values (27) 787955
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1852582
77.1%
Uppercase Letter 349673
 
14.6%
Space Separator 199683
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 273856
14.8%
n 199165
10.8%
e 182900
9.9%
i 162552
8.8%
s 156547
8.5%
r 138704
 
7.5%
l 124173
 
6.7%
g 90265
 
4.9%
h 86138
 
4.6%
o 69202
 
3.7%
Other values (11) 369080
19.9%
Uppercase Letter
ValueCountFrequency (%)
K 65029
18.6%
R 60719
17.4%
D 40003
11.4%
C 39655
11.3%
I 32402
9.3%
S 23750
 
6.8%
P 23052
 
6.6%
M 16735
 
4.8%
X 15667
 
4.5%
B 15552
 
4.4%
Other values (5) 17109
 
4.9%
Space Separator
ValueCountFrequency (%)
199683
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2202255
91.7%
Common 199683
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 273856
 
12.4%
n 199165
 
9.0%
e 182900
 
8.3%
i 162552
 
7.4%
s 156547
 
7.1%
r 138704
 
6.3%
l 124173
 
5.6%
g 90265
 
4.1%
h 86138
 
3.9%
o 69202
 
3.1%
Other values (26) 718753
32.6%
Common
ValueCountFrequency (%)
199683
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2401938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 273856
 
11.4%
199683
 
8.3%
n 199165
 
8.3%
e 182900
 
7.6%
i 162552
 
6.8%
s 156547
 
6.5%
r 138704
 
5.8%
l 124173
 
5.2%
g 90265
 
3.8%
h 86138
 
3.6%
Other values (27) 787955
32.8%

over
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.149409
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-04-18T20:20:53.348462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6780903
Coefficient of variation (CV)0.55945036
Kurtosis-1.1820232
Mean10.149409
Median Absolute Deviation (MAD)5
Skewness0.052481239
Sum1363299
Variance32.240709
MonotonicityNot monotonic
2025-04-18T20:20:53.455496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 7235
 
5.4%
2 7148
 
5.3%
3 7079
 
5.3%
4 7044
 
5.2%
5 7019
 
5.2%
6 7012
 
5.2%
7 6979
 
5.2%
8 6962
 
5.2%
9 6923
 
5.2%
10 6883
 
5.1%
Other values (10) 64039
47.7%
ValueCountFrequency (%)
1 7235
5.4%
2 7148
5.3%
3 7079
5.3%
4 7044
5.2%
5 7019
5.2%
6 7012
5.2%
7 6979
5.2%
8 6962
5.2%
9 6923
5.2%
10 6883
5.1%
ValueCountFrequency (%)
20 5086
3.8%
19 5876
4.4%
18 6224
4.6%
17 6454
4.8%
16 6548
4.9%
15 6664
5.0%
14 6726
5.0%
13 6803
5.1%
12 6819
5.1%
11 6839
5.1%

ball
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6161566
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-04-18T20:20:53.569330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8072959
Coefficient of variation (CV)0.49978363
Kurtosis-1.083561
Mean3.6161566
Median Absolute Deviation (MAD)2
Skewness0.09612598
Sum485733
Variance3.2663184
MonotonicityNot monotonic
2025-04-18T20:20:53.654126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 21781
16.2%
2 21723
16.2%
3 21660
16.1%
4 21608
16.1%
5 21537
16.0%
6 21464
16.0%
7 3865
 
2.9%
8 595
 
0.4%
9 90
 
0.1%
ValueCountFrequency (%)
1 21781
16.2%
2 21723
16.2%
3 21660
16.1%
4 21608
16.1%
5 21537
16.0%
6 21464
16.0%
7 3865
 
2.9%
8 595
 
0.4%
9 90
 
0.1%
ValueCountFrequency (%)
9 90
 
0.1%
8 595
 
0.4%
7 3865
 
2.9%
6 21464
16.0%
5 21537
16.0%
4 21608
16.1%
3 21660
16.1%
2 21723
16.2%
1 21781
16.2%
Distinct449
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2025-04-18T20:20:53.999622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length17
Mean length9.4154389
Min length5

Characters and Unicode

Total characters1264710
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowDA Warner
2nd rowDA Warner
3rd rowDA Warner
4th rowDA Warner
5th rowDA Warner
ValueCountFrequency (%)
s 4780
 
1.7%
v 4702
 
1.7%
singh 3821
 
1.4%
sr 3791
 
1.4%
sharma 3513
 
1.3%
m 3467
 
1.3%
r 3349
 
1.2%
smith 3329
 
1.2%
da 3267
 
1.2%
sk 3248
 
1.2%
Other values (626) 236993
86.4%
2025-04-18T20:20:54.459613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 140994
 
11.1%
139937
 
11.1%
i 61602
 
4.9%
h 58098
 
4.6%
r 56602
 
4.5%
n 56570
 
4.5%
e 51277
 
4.1%
S 49974
 
4.0%
l 46137
 
3.6%
M 35190
 
2.8%
Other values (44) 568329
44.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 737712
58.3%
Uppercase Letter 386858
30.6%
Space Separator 139937
 
11.1%
Dash Punctuation 203
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 140994
19.1%
i 61602
 
8.4%
h 58098
 
7.9%
r 56602
 
7.7%
n 56570
 
7.7%
e 51277
 
7.0%
l 46137
 
6.3%
s 33111
 
4.5%
t 27024
 
3.7%
o 26854
 
3.6%
Other values (16) 179443
24.3%
Uppercase Letter
ValueCountFrequency (%)
S 49974
12.9%
M 35190
 
9.1%
R 31486
 
8.1%
A 30015
 
7.8%
K 29222
 
7.6%
D 27818
 
7.2%
P 25326
 
6.5%
J 19993
 
5.2%
G 19253
 
5.0%
V 17603
 
4.6%
Other values (16) 100978
26.1%
Space Separator
ValueCountFrequency (%)
139937
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1124570
88.9%
Common 140140
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 140994
 
12.5%
i 61602
 
5.5%
h 58098
 
5.2%
r 56602
 
5.0%
n 56570
 
5.0%
e 51277
 
4.6%
S 49974
 
4.4%
l 46137
 
4.1%
M 35190
 
3.1%
s 33111
 
2.9%
Other values (42) 535015
47.6%
Common
ValueCountFrequency (%)
139937
99.9%
- 203
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1264710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 140994
 
11.1%
139937
 
11.1%
i 61602
 
4.9%
h 58098
 
4.6%
r 56602
 
4.5%
n 56570
 
4.5%
e 51277
 
4.1%
S 49974
 
4.0%
l 46137
 
3.6%
M 35190
 
2.8%
Other values (44) 568329
44.9%
Distinct447
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2025-04-18T20:20:54.813529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length17
Mean length9.4171214
Min length2

Characters and Unicode

Total characters1264936
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowS Dhawan
2nd rowS Dhawan
3rd rowS Dhawan
4th rowS Dhawan
5th rowS Dhawan
ValueCountFrequency (%)
s 4826
 
1.8%
v 4629
 
1.7%
sr 4056
 
1.5%
singh 3627
 
1.3%
m 3577
 
1.3%
sharma 3574
 
1.3%
r 3343
 
1.2%
sk 3247
 
1.2%
mk 3236
 
1.2%
g 3169
 
1.2%
Other values (625) 236947
86.4%
2025-04-18T20:20:55.297765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 141742
 
11.2%
139909
 
11.1%
i 61354
 
4.9%
h 57725
 
4.6%
n 56738
 
4.5%
r 56624
 
4.5%
e 52095
 
4.1%
S 49720
 
3.9%
l 45535
 
3.6%
M 35623
 
2.8%
Other values (44) 567871
44.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 737684
58.3%
Uppercase Letter 387128
30.6%
Space Separator 139909
 
11.1%
Dash Punctuation 215
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 141742
19.2%
i 61354
 
8.3%
h 57725
 
7.8%
n 56738
 
7.7%
r 56624
 
7.7%
e 52095
 
7.1%
l 45535
 
6.2%
s 33024
 
4.5%
u 26733
 
3.6%
y 26648
 
3.6%
Other values (16) 179466
24.3%
Uppercase Letter
ValueCountFrequency (%)
S 49720
12.8%
M 35623
 
9.2%
R 31869
 
8.2%
A 30066
 
7.8%
K 29002
 
7.5%
D 27382
 
7.1%
P 25104
 
6.5%
J 19997
 
5.2%
G 19478
 
5.0%
V 17642
 
4.6%
Other values (16) 101245
26.2%
Space Separator
ValueCountFrequency (%)
139909
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1124812
88.9%
Common 140124
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 141742
 
12.6%
i 61354
 
5.5%
h 57725
 
5.1%
n 56738
 
5.0%
r 56624
 
5.0%
e 52095
 
4.6%
S 49720
 
4.4%
l 45535
 
4.0%
M 35623
 
3.2%
s 33024
 
2.9%
Other values (42) 534632
47.5%
Common
ValueCountFrequency (%)
139909
99.8%
- 215
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1264936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 141742
 
11.2%
139909
 
11.1%
i 61354
 
4.9%
h 57725
 
4.6%
n 56738
 
4.5%
r 56624
 
4.5%
e 52095
 
4.1%
S 49720
 
3.9%
l 45535
 
3.6%
M 35623
 
2.8%
Other values (44) 567871
44.9%

bowler
Text

Distinct347
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Memory size1.0 MiB
2025-04-18T20:20:55.681981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length16
Mean length9.4429059
Min length5

Characters and Unicode

Total characters1268390
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTS Mills
2nd rowTS Mills
3rd rowTS Mills
4th rowTS Mills
5th rowTS Mills
ValueCountFrequency (%)
singh 8041
 
2.9%
r 7911
 
2.9%
a 7292
 
2.7%
sharma 6980
 
2.5%
kumar 5957
 
2.2%
m 4618
 
1.7%
pp 4316
 
1.6%
s 4153
 
1.5%
p 3994
 
1.5%
sk 3616
 
1.3%
Other values (505) 216884
79.2%
2025-04-18T20:20:56.179035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 160272
 
12.6%
139440
 
11.0%
n 69226
 
5.5%
r 67262
 
5.3%
h 61754
 
4.9%
i 56653
 
4.5%
e 54444
 
4.3%
S 49820
 
3.9%
l 42484
 
3.3%
M 35276
 
2.8%
Other values (45) 531759
41.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 767604
60.5%
Uppercase Letter 360778
28.4%
Space Separator 139440
 
11.0%
Dash Punctuation 493
 
< 0.1%
Open Punctuation 25
 
< 0.1%
Decimal Number 25
 
< 0.1%
Close Punctuation 25
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 160272
20.9%
n 69226
9.0%
r 67262
8.8%
h 61754
 
8.0%
i 56653
 
7.4%
e 54444
 
7.1%
l 42484
 
5.5%
t 30636
 
4.0%
o 29626
 
3.9%
m 27371
 
3.6%
Other values (16) 167876
21.9%
Uppercase Letter
ValueCountFrequency (%)
S 49820
13.8%
M 35276
9.8%
P 33168
 
9.2%
A 31935
 
8.9%
K 27158
 
7.5%
R 24757
 
6.9%
J 22539
 
6.2%
B 18349
 
5.1%
D 17399
 
4.8%
C 12599
 
3.5%
Other values (14) 87778
24.3%
Space Separator
ValueCountFrequency (%)
139440
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 493
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Decimal Number
ValueCountFrequency (%)
2 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1128382
89.0%
Common 140008
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 160272
 
14.2%
n 69226
 
6.1%
r 67262
 
6.0%
h 61754
 
5.5%
i 56653
 
5.0%
e 54444
 
4.8%
S 49820
 
4.4%
l 42484
 
3.8%
M 35276
 
3.1%
P 33168
 
2.9%
Other values (40) 498023
44.1%
Common
ValueCountFrequency (%)
139440
99.6%
- 493
 
0.4%
( 25
 
< 0.1%
2 25
 
< 0.1%
) 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1268390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 160272
 
12.6%
139440
 
11.0%
n 69226
 
5.5%
r 67262
 
5.3%
h 61754
 
4.9%
i 56653
 
4.5%
e 54444
 
4.3%
S 49820
 
3.9%
l 42484
 
3.3%
M 35276
 
2.8%
Other values (45) 531759
41.9%

is_super_over
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.0 MiB
0.0
134241 
1.0
 
81

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters402966
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 134241
99.9%
1.0 81
 
0.1%
(Missing) 1
 
< 0.1%

Length

2025-04-18T20:20:56.299148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T20:20:56.364604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 134241
99.9%
1.0 81
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 268563
66.6%
. 134322
33.3%
1 81
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 268644
66.7%
Other Punctuation 134322
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 268563
> 99.9%
1 81
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 134322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 402966
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 268563
66.6%
. 134322
33.3%
1 81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 402966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 268563
66.6%
. 134322
33.3%
1 81
 
< 0.1%

wide_runs
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.037343101
Minimum0
Maximum5
Zeros130254
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-04-18T20:20:56.425761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25657844
Coefficient of variation (CV)6.8708393
Kurtosis189.51027
Mean0.037343101
Median Absolute Deviation (MAD)0
Skewness11.677653
Sum5016
Variance0.065832498
MonotonicityNot monotonic
2025-04-18T20:20:56.515754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 130254
97.0%
1 3668
 
2.7%
2 194
 
0.1%
5 169
 
0.1%
3 33
 
< 0.1%
4 4
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 130254
97.0%
1 3668
 
2.7%
2 194
 
0.1%
3 33
 
< 0.1%
4 4
 
< 0.1%
5 169
 
0.1%
ValueCountFrequency (%)
5 169
 
0.1%
4 4
 
< 0.1%
3 33
 
< 0.1%
2 194
 
0.1%
1 3668
 
2.7%
0 130254
97.0%

bye_runs
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.0 MiB
0.0
133943 
1.0
 
263
4.0
 
89
2.0
 
26
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters402966
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 133943
99.7%
1.0 263
 
0.2%
4.0 89
 
0.1%
2.0 26
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2025-04-18T20:20:56.633896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T20:20:56.710222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 133943
99.7%
1.0 263
 
0.2%
4.0 89
 
0.1%
2.0 26
 
< 0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 268265
66.6%
. 134322
33.3%
1 263
 
0.1%
4 89
 
< 0.1%
2 26
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 268644
66.7%
Other Punctuation 134322
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 268265
99.9%
1 263
 
0.1%
4 89
 
< 0.1%
2 26
 
< 0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 134322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 402966
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 268265
66.6%
. 134322
33.3%
1 263
 
0.1%
4 89
 
< 0.1%
2 26
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 402966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 268265
66.6%
. 134322
33.3%
1 263
 
0.1%
4 89
 
< 0.1%
2 26
 
< 0.1%
3 1
 
< 0.1%

legbye_runs
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.022728965
Minimum0
Maximum5
Zeros131967
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-04-18T20:20:56.788077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20297019
Coefficient of variation (CV)8.9300235
Kurtosis225.52545
Mean0.022728965
Median Absolute Deviation (MAD)0
Skewness13.31632
Sum3053
Variance0.041196898
MonotonicityNot monotonic
2025-04-18T20:20:56.872107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 131967
98.2%
1 2043
 
1.5%
4 179
 
0.1%
2 113
 
0.1%
3 16
 
< 0.1%
5 4
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 131967
98.2%
1 2043
 
1.5%
2 113
 
0.1%
3 16
 
< 0.1%
4 179
 
0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
5 4
 
< 0.1%
4 179
 
0.1%
3 16
 
< 0.1%
2 113
 
0.1%
1 2043
 
1.5%
0 131967
98.2%

noball_runs
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.0 MiB
0.0
133762 
1.0
 
546
2.0
 
9
5.0
 
4
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters402966
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 133762
99.6%
1.0 546
 
0.4%
2.0 9
 
< 0.1%
5.0 4
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2025-04-18T20:20:56.970762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T20:20:57.050937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 133762
99.6%
1.0 546
 
0.4%
2.0 9
 
< 0.1%
5.0 4
 
< 0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 268084
66.5%
. 134322
33.3%
1 546
 
0.1%
2 9
 
< 0.1%
5 4
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 268644
66.7%
Other Punctuation 134322
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 268084
99.8%
1 546
 
0.2%
2 9
 
< 0.1%
5 4
 
< 0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 134322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 402966
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 268084
66.5%
. 134322
33.3%
1 546
 
0.1%
2 9
 
< 0.1%
5 4
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 402966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 268084
66.5%
. 134322
33.3%
1 546
 
0.1%
2 9
 
< 0.1%
5 4
 
< 0.1%
3 1
 
< 0.1%

penalty_runs
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.0 MiB
0.0
134320 
5.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters402966
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 134320
> 99.9%
5.0 2
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2025-04-18T20:20:57.156000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T20:20:57.231093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 134320
> 99.9%
5.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 268642
66.7%
. 134322
33.3%
5 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 268644
66.7%
Other Punctuation 134322
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 268642
> 99.9%
5 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 134322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 402966
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 268642
66.7%
. 134322
33.3%
5 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 402966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 268642
66.7%
. 134322
33.3%
5 2
 
< 0.1%

batsman_runs
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.2163681
Minimum0
Maximum6
Zeros55004
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-04-18T20:20:57.324908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5930634
Coefficient of variation (CV)1.3096886
Kurtosis1.7122981
Mean1.2163681
Median Absolute Deviation (MAD)1
Skewness1.6017378
Sum163385
Variance2.5378511
MonotonicityNot monotonic
2025-04-18T20:20:57.453100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 55004
40.9%
1 49305
36.7%
4 15165
 
11.3%
2 8561
 
6.4%
6 5785
 
4.3%
3 461
 
0.3%
5 41
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 55004
40.9%
1 49305
36.7%
2 8561
 
6.4%
3 461
 
0.3%
4 15165
 
11.3%
5 41
 
< 0.1%
6 5785
 
4.3%
ValueCountFrequency (%)
6 5785
 
4.3%
5 41
 
< 0.1%
4 15165
 
11.3%
3 461
 
0.3%
2 8561
 
6.4%
1 49305
36.7%
0 55004
40.9%

extra_runs
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.069534402
Minimum0
Maximum7
Zeros126959
Zeros (%)94.5%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-04-18T20:20:57.572560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35095829
Coefficient of variation (CV)5.0472612
Kurtosis88.179055
Mean0.069534402
Median Absolute Deviation (MAD)0
Skewness8.1168916
Sum9340
Variance0.12317172
MonotonicityNot monotonic
2025-04-18T20:20:57.703392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 126959
94.5%
1 6520
 
4.9%
2 341
 
0.3%
4 272
 
0.2%
5 178
 
0.1%
3 51
 
< 0.1%
7 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 126959
94.5%
1 6520
 
4.9%
2 341
 
0.3%
3 51
 
< 0.1%
4 272
 
0.2%
5 178
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 178
 
0.1%
4 272
 
0.2%
3 51
 
< 0.1%
2 341
 
0.3%
1 6520
 
4.9%
0 126959
94.5%

total_runs
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.2859025
Minimum0
Maximum7
Zeros47961
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-04-18T20:20:58.219764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5820026
Coefficient of variation (CV)1.2302663
Kurtosis1.6421943
Mean1.2859025
Median Absolute Deviation (MAD)1
Skewness1.567966
Sum172725
Variance2.5027321
MonotonicityNot monotonic
2025-04-18T20:20:58.346120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 55317
41.2%
0 47961
35.7%
4 15370
 
11.4%
2 9061
 
6.7%
6 5749
 
4.3%
3 541
 
0.4%
5 286
 
0.2%
7 37
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 47961
35.7%
1 55317
41.2%
2 9061
 
6.7%
3 541
 
0.4%
4 15370
 
11.4%
5 286
 
0.2%
6 5749
 
4.3%
7 37
 
< 0.1%
ValueCountFrequency (%)
7 37
 
< 0.1%
6 5749
 
4.3%
5 286
 
0.2%
4 15370
 
11.4%
3 541
 
0.4%
2 9061
 
6.7%
1 55317
41.2%
0 47961
35.7%

player_dismissed
Text

Missing 

Distinct425
Distinct (%)6.4%
Missing127664
Missing (%)95.0%
Memory size1.0 MiB
2025-04-18T20:20:58.907598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length17
Mean length9.4488662
Min length5

Characters and Unicode

Total characters62920
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)1.1%

Sample

1st rowDA Warner
2nd rowS Dhawan
3rd rowMC Henriques
4th rowYuvraj Singh
5th rowMandeep Singh
ValueCountFrequency (%)
singh 257
 
1.9%
s 218
 
1.6%
r 206
 
1.5%
v 206
 
1.5%
m 189
 
1.4%
sharma 185
 
1.4%
pathan 146
 
1.1%
sk 144
 
1.1%
sr 140
 
1.0%
ms 135
 
1.0%
Other values (594) 11787
86.6%
2025-04-18T20:20:59.688698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7212
 
11.5%
6954
 
11.1%
i 3041
 
4.8%
h 3006
 
4.8%
r 2864
 
4.6%
n 2840
 
4.5%
e 2539
 
4.0%
S 2445
 
3.9%
l 2183
 
3.5%
M 1730
 
2.7%
Other values (44) 28106
44.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36951
58.7%
Uppercase Letter 18993
30.2%
Space Separator 6954
 
11.1%
Dash Punctuation 22
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7212
19.5%
i 3041
 
8.2%
h 3006
 
8.1%
r 2864
 
7.8%
n 2840
 
7.7%
e 2539
 
6.9%
l 2183
 
5.9%
s 1526
 
4.1%
t 1410
 
3.8%
o 1353
 
3.7%
Other values (16) 8977
24.3%
Uppercase Letter
ValueCountFrequency (%)
S 2445
12.9%
M 1730
 
9.1%
A 1557
 
8.2%
R 1539
 
8.1%
K 1409
 
7.4%
P 1336
 
7.0%
D 1253
 
6.6%
J 1008
 
5.3%
V 865
 
4.6%
G 857
 
4.5%
Other values (16) 4994
26.3%
Space Separator
ValueCountFrequency (%)
6954
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55944
88.9%
Common 6976
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7212
 
12.9%
i 3041
 
5.4%
h 3006
 
5.4%
r 2864
 
5.1%
n 2840
 
5.1%
e 2539
 
4.5%
S 2445
 
4.4%
l 2183
 
3.9%
M 1730
 
3.1%
A 1557
 
2.8%
Other values (42) 26527
47.4%
Common
ValueCountFrequency (%)
6954
99.7%
- 22
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7212
 
11.5%
6954
 
11.1%
i 3041
 
4.8%
h 3006
 
4.8%
r 2864
 
4.6%
n 2840
 
4.5%
e 2539
 
4.0%
S 2445
 
3.9%
l 2183
 
3.5%
M 1730
 
2.7%
Other values (44) 28106
44.7%

dismissal_kind
Categorical

High correlation  Missing 

Distinct9
Distinct (%)0.1%
Missing127664
Missing (%)95.0%
Memory size1.0 MiB
caught
3895 
bowled
1259 
run out
669 
lbw
410 
stumped
 
218
Other values (4)
 
208

Length

Max length21
Median length6
Mean length6.2803724
Min length3

Characters and Unicode

Total characters41821
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcaught
2nd rowcaught
3rd rowcaught
4th rowbowled
5th rowbowled

Common Values

ValueCountFrequency (%)
caught 3895
 
2.9%
bowled 1259
 
0.9%
run out 669
 
0.5%
lbw 410
 
0.3%
stumped 218
 
0.2%
caught and bowled 193
 
0.1%
retired hurt 8
 
< 0.1%
hit wicket 6
 
< 0.1%
obstructing the field 1
 
< 0.1%
(Missing) 127664
95.0%

Length

2025-04-18T20:20:59.829499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T20:20:59.930714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
caught 4088
52.9%
bowled 1452
 
18.8%
run 669
 
8.7%
out 669
 
8.7%
lbw 410
 
5.3%
stumped 218
 
2.8%
and 193
 
2.5%
retired 8
 
0.1%
hurt 8
 
0.1%
hit 6
 
0.1%
Other values (4) 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
u 5653
13.5%
t 5006
12.0%
a 4281
10.2%
h 4103
9.8%
c 4095
9.8%
g 4089
9.8%
o 2122
 
5.1%
d 1872
 
4.5%
w 1868
 
4.5%
b 1863
 
4.5%
Other values (11) 6869
16.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40750
97.4%
Space Separator 1071
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 5653
13.9%
t 5006
12.3%
a 4281
10.5%
h 4103
10.1%
c 4095
10.0%
g 4089
10.0%
o 2122
 
5.2%
d 1872
 
4.6%
w 1868
 
4.6%
b 1863
 
4.6%
Other values (10) 5798
14.2%
Space Separator
ValueCountFrequency (%)
1071
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40750
97.4%
Common 1071
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 5653
13.9%
t 5006
12.3%
a 4281
10.5%
h 4103
10.1%
c 4095
10.0%
g 4089
10.0%
o 2122
 
5.2%
d 1872
 
4.6%
w 1868
 
4.6%
b 1863
 
4.6%
Other values (10) 5798
14.2%
Common
ValueCountFrequency (%)
1071
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 5653
13.5%
t 5006
12.0%
a 4281
10.2%
h 4103
9.8%
c 4095
9.8%
g 4089
9.8%
o 2122
 
5.1%
d 1872
 
4.5%
w 1868
 
4.5%
b 1863
 
4.5%
Other values (11) 6869
16.4%

fielder
Text

Missing 

Distinct436
Distinct (%)9.1%
Missing129543
Missing (%)96.4%
Memory size1.0 MiB
2025-04-18T20:21:00.316539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length20
Mean length9.5361925
Min length5

Characters and Unicode

Total characters45583
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)1.8%

Sample

1st rowMandeep Singh
2nd rowSachin Baby
3rd rowSachin Baby
4th rowDA Warner
5th rowBCJ Cutting
ValueCountFrequency (%)
m 170
 
1.7%
singh 163
 
1.7%
r 154
 
1.6%
ms 144
 
1.5%
sharma 144
 
1.5%
s 122
 
1.2%
karthik 120
 
1.2%
patel 119
 
1.2%
sk 112
 
1.1%
v 112
 
1.1%
Other values (560) 8512
86.2%
2025-04-18T20:21:00.819157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5231
 
11.5%
5092
 
11.2%
i 2293
 
5.0%
h 2223
 
4.9%
r 2064
 
4.5%
n 2021
 
4.4%
e 1777
 
3.9%
S 1710
 
3.8%
l 1580
 
3.5%
M 1258
 
2.8%
Other values (45) 20334
44.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26748
58.7%
Uppercase Letter 13594
29.8%
Space Separator 5092
 
11.2%
Open Punctuation 69
 
0.2%
Close Punctuation 69
 
0.2%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5231
19.6%
i 2293
 
8.6%
h 2223
 
8.3%
r 2064
 
7.7%
n 2021
 
7.6%
e 1777
 
6.6%
l 1580
 
5.9%
t 1130
 
4.2%
s 1125
 
4.2%
o 991
 
3.7%
Other values (16) 6313
23.6%
Uppercase Letter
ValueCountFrequency (%)
S 1710
12.6%
M 1258
 
9.3%
A 1150
 
8.5%
K 1132
 
8.3%
R 1057
 
7.8%
P 1006
 
7.4%
D 971
 
7.1%
J 686
 
5.0%
B 637
 
4.7%
V 627
 
4.6%
Other values (15) 3360
24.7%
Space Separator
ValueCountFrequency (%)
5092
100.0%
Open Punctuation
ValueCountFrequency (%)
( 69
100.0%
Close Punctuation
ValueCountFrequency (%)
) 69
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40342
88.5%
Common 5241
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5231
 
13.0%
i 2293
 
5.7%
h 2223
 
5.5%
r 2064
 
5.1%
n 2021
 
5.0%
e 1777
 
4.4%
S 1710
 
4.2%
l 1580
 
3.9%
M 1258
 
3.1%
A 1150
 
2.9%
Other values (41) 19035
47.2%
Common
ValueCountFrequency (%)
5092
97.2%
( 69
 
1.3%
) 69
 
1.3%
- 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5231
 
11.5%
5092
 
11.2%
i 2293
 
5.0%
h 2223
 
4.9%
r 2064
 
4.5%
n 2021
 
4.4%
e 1777
 
3.9%
S 1710
 
3.8%
l 1580
 
3.5%
M 1258
 
2.8%
Other values (45) 20334
44.6%

Interactions

2025-04-18T20:20:49.686003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:41.664722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:42.634696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:43.659732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:45.056720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:46.465165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:47.666417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:48.662001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:49.823151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:41.797771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:42.749458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:43.785078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:45.247806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:46.647386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:47.785620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:48.793863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:49.971542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:41.932312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:42.879729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:43.933477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:45.435815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:46.834718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:47.909084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:48.917093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:50.400990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:42.063716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:43.013897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:44.062941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:45.618810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:47.012965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:48.041856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:49.056202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:50.519094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:42.169437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:43.141744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:44.201124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:45.770416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:47.182062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:48.160359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:49.175465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:50.640316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:42.285659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:43.282282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:44.322885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:45.931878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:47.321207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:48.286961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:49.295931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:50.760684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:42.401116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:43.406820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:44.692698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:46.090409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:47.435604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:48.416793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:49.433392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:50.880575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:42.518944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:43.538245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:44.872913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:46.283207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:47.550263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:48.540407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T20:20:49.560228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-18T20:21:00.932052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ballbatsman_runsbatting_teambowling_teambye_runsdismissal_kindextra_runsinningis_super_overlegbye_runsmatch_idnoball_runsoverpenalty_runstotal_runswide_runs
ball1.0000.0100.0000.0000.0010.019-0.0060.0000.000-0.007-0.0030.003-0.0110.0000.008-0.007
batsman_runs0.0101.0000.0150.0130.0310.376-0.2430.0080.012-0.1460.0000.0040.1390.0000.936-0.193
batting_team0.0000.0151.0000.1270.0030.0150.0050.0750.0210.0000.2350.0020.0000.0050.0140.004
bowling_team0.0000.0130.1271.0000.0030.0270.0060.0790.0170.0040.2350.0030.0000.0000.0110.007
bye_runs0.0010.0310.0030.0031.0001.0000.3390.0060.0020.0000.0060.0000.0290.0000.0550.000
dismissal_kind0.0190.3760.0150.0271.0001.0000.2490.0160.0000.3180.0110.0530.0731.0000.3770.240
extra_runs-0.006-0.2430.0050.0060.3390.2491.0000.0000.0060.555-0.0110.192-0.0050.7090.1000.733
inning0.0000.0080.0750.0790.0060.0160.0001.0001.0000.0000.0080.0090.0540.0000.0110.000
is_super_over0.0000.0120.0210.0170.0020.0000.0061.0001.0000.0000.0140.0170.0700.0000.0170.000
legbye_runs-0.007-0.1460.0000.0040.0000.3180.5550.0000.0001.000-0.0020.000-0.0020.0000.051-0.024
match_id-0.0030.0000.2350.2350.0060.011-0.0110.0080.014-0.0021.0000.0070.0080.000-0.003-0.009
noball_runs0.0030.0040.0020.0030.0000.0530.1920.0090.0170.0000.0071.0000.0180.0000.1710.000
over-0.0110.1390.0000.0000.0290.073-0.0050.0540.070-0.0020.0080.0181.0000.0000.137-0.020
penalty_runs0.0000.0000.0050.0000.0001.0000.7090.0000.0000.0000.0000.0000.0001.0000.1230.050
total_runs0.0080.9360.0140.0110.0550.3770.1000.0110.0170.051-0.0030.1710.1370.1231.0000.061
wide_runs-0.007-0.1930.0040.0070.0000.2400.7330.0000.000-0.024-0.0090.000-0.0200.0500.0611.000

Missing values

2025-04-18T20:20:51.129802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-18T20:20:51.585234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-18T20:20:52.283085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder
011Sunrisers HyderabadRoyal Challengers Bangalore11DA WarnerS DhawanTS Mills0.00.00.00.00.00.00.00.00.0NaNNaNNaN
111Sunrisers HyderabadRoyal Challengers Bangalore12DA WarnerS DhawanTS Mills0.00.00.00.00.00.00.00.00.0NaNNaNNaN
211Sunrisers HyderabadRoyal Challengers Bangalore13DA WarnerS DhawanTS Mills0.00.00.00.00.00.04.00.04.0NaNNaNNaN
311Sunrisers HyderabadRoyal Challengers Bangalore14DA WarnerS DhawanTS Mills0.00.00.00.00.00.00.00.00.0NaNNaNNaN
411Sunrisers HyderabadRoyal Challengers Bangalore15DA WarnerS DhawanTS Mills0.02.00.00.00.00.00.02.02.0NaNNaNNaN
511Sunrisers HyderabadRoyal Challengers Bangalore16S DhawanDA WarnerTS Mills0.00.00.00.00.00.00.00.00.0NaNNaNNaN
611Sunrisers HyderabadRoyal Challengers Bangalore17S DhawanDA WarnerTS Mills0.00.00.01.00.00.00.01.01.0NaNNaNNaN
711Sunrisers HyderabadRoyal Challengers Bangalore21S DhawanDA WarnerA Choudhary0.00.00.00.00.00.01.00.01.0NaNNaNNaN
811Sunrisers HyderabadRoyal Challengers Bangalore22DA WarnerS DhawanA Choudhary0.00.00.00.00.00.04.00.04.0NaNNaNNaN
911Sunrisers HyderabadRoyal Challengers Bangalore23DA WarnerS DhawanA Choudhary0.00.00.00.01.00.00.01.01.0NaNNaNNaN
match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder
1343135671Mumbai IndiansKolkata Knight Riders203HH PandyaKA PollardUT Yadav0.00.00.00.00.00.01.00.01.0NaNNaNNaN
1343145671Mumbai IndiansKolkata Knight Riders204KA PollardHH PandyaUT Yadav0.00.00.00.00.00.02.00.02.0NaNNaNNaN
1343155671Mumbai IndiansKolkata Knight Riders205KA PollardHH PandyaUT Yadav0.00.00.00.00.00.01.00.01.0NaNNaNNaN
1343165671Mumbai IndiansKolkata Knight Riders206HH PandyaKA PollardUT Yadav0.00.00.00.00.00.01.00.01.0NaNNaNNaN
1343175671Mumbai IndiansKolkata Knight Riders207KA PollardHH PandyaUT Yadav0.00.00.00.00.00.01.00.01.0NaNNaNNaN
1343185672Kolkata Knight RidersMumbai Indians11RV UthappaG GambhirSL Malinga0.00.00.00.00.00.02.00.02.0NaNNaNNaN
1343195672Kolkata Knight RidersMumbai Indians12RV UthappaG GambhirSL Malinga0.00.00.00.00.00.04.00.04.0NaNNaNNaN
1343205672Kolkata Knight RidersMumbai Indians13RV UthappaG GambhirSL Malinga0.00.00.00.00.00.00.00.00.0NaNNaNNaN
1343215672Kolkata Knight RidersMumbai Indians14RV UthappaG GambhirSL Malinga0.00.00.00.00.00.00.00.00.0NaNNaNNaN
1343225672Kolkata Knight RidersMumbai Indians15RV UthappaGNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder# duplicates
02211Mumbai IndiansDelhi Daredevils41SR TendulkarC MadanPJ Sangwan0.00.00.00.00.00.01.00.01.0NaNNaNNaN2